Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 49 for “"Multi-class classification"”.
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Multi-Class Classification in Natural Language Processing
… this thesis as an extension of the current classification methods which aim at disambiguating among many classes.
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Investigating Ensembles of Single-class Classifiers for Multi-class Classification
Traditional methods of multi-class classification in machine learning involve the use of a monolithic feature extractor and classifier head trained on data from all of the classes at once. These architectures (especially the classifier head) are dependent on the number and types of classes, and are …
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Subspace and graph methods to leverage auxiliary data for limited target data multi-class classification, applied to speaker verification
Multi-class classification can be adversely affected by the absence of sufficient target (in-class) instances for training. Such cases arise in face recognition, speaker verification, and document classification, among others. Auxiliary data-sets, which contain a diverse sampling of non-target …
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Machine Learning-based Feature Selection and Optimisation for Clinical Decision Support Systems. Optimal Data-driven Feature Selection Methods for Binary and Multi-class Classification Problems: Towards a Minimum Viable Solution for Predicting Early Diagnosis and Prognosis
… for increasing the performance of ML-based classifiers for several applications. Although advances in ML, such as Deep Learning (DL), have denied the usefulness of any extrinsic FS by incorporating it in their architectures, e.g., convolutional filters in convolutional neural networks, DL …
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Machine Learning-based Feature Selection and Optimisation for Clinical Decision Support Systems. Optimal Data-driven Feature Selection Methods for Binary and Multi-class Classification Problems: Towards a Minimum Viable Solution for Predicting Early Diagnosis and Prognosis
… for increasing the performance of ML-based classifiers for several applications. Although advances in ML, such as Deep Learning (DL), have denied the usefulness of any extrinsic FS by incorporating it in their architectures, e.g., convolutional filters in convolutional neural networks, DL …
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Automatinis užduočių apimties vetinimas naudojant natūralios kalbos apdorojimo įrankius /
… Research is made to justify this claim, where a classic perceptron based machine learning architecture is compared against newer, transformer-based architectures. In this research, the task effort estimation problem is modeled on each of the selected architectures to check which of them are the …
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Contributions to Efficient Statistical Modeling of Complex Data with Temporal Structures
… projects: Neighborhood vector auto regression in multivariate time series, uncertainty quantification for agent-based modeling networked anagrams, and a scalable algorithm for multi-class classification. The first project studies the modeling of multivariate time series, with the applications in …
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Galaxy classification with deep convolutional neural networks
Galaxy classification, using digital images captured from sky surveys to determine the galaxy morphological classes, is of great interest to astronomy researchers. Conventional methods rely heavily on a few handcrafted morphological features while popular feature extraction methods that developed …
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Streaming Random Forests
… rates. We consider the problem of data-stream classification, introducing an online and incremental stream-classification ensemble algorithm, Streaming Random Forests, an extension of the Random Forests algorithm by Breiman, which is a standard classification algorithm. Our algorithm is …
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FUNCTIONAL STATISTICAL LEARNING METHODS APPLIED TO HUMAN EMOTION RECOGNITION FROM FACIAL VIDEOS
… research methods. Our approach employs multivariate function-on-scalar regression models and functional analysis of variance (FANOVA) to effectively separate shared information from group-specific influences and individual noise through paired group comparisons, even with limited sample …
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Pattern recognition of brain fMRI images for various physiological states
… conditions were used involving both binary and multi-class classification. Bilateral finger tapping data which had two distinct states "Active" and "Rest" were used for binary classification. Binary classification was done using Learning Vector Quantization (LVQ) and Least Square Support Vector …
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Designing a resource-allocating codebook for patch-based visual object recognition
… vector in histogram space to which standard classifiers can be directly applied. The discriminative power of a visual codebook determines the quality of the codebook model, whereas the size of the codebook controls the complexity of the model. Thus, the construction of a codebook plays a …
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Machine Learning for Information Extraction
… dissertation studies part of speech tagging as a multi-class classification problem, and applies the SNOW (Sparse Network of Winnows) learning system to learn a part of speech classifier. A comprehensive experimental evaluation of the system confirms that it is appropriate for NLP applications. …
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Discipline-Independent Text Information Extraction from Heterogeneous Styled References Using Knowledge from the Web
… learning. In particular, we research a two-stage classifier approach, with multi-class classification with respect to reference styles, and partially solve the problem of parsing surface representations of references. We describe empirical evidence for the effectiveness of our approach and plans …
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Latent variable augmentation for approximate Bayesian inference
… for different Gaussian Process models such as classification and multi-class classification. We focus on the effects on inference and develop a generalization for a given class of likelihoods. We show that augmentations are scalable with data and outperform all existing methods in terms of …
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Analysis of Colorectal Polyps in Optical Projection Tomography
… methods, as well as weakly supervised classification methods, for the diagnostic task of discriminating levels of dysplastic change.<br/><br/>Firstly, we build a patch-based recognition system and evaluate both multi-class classification and ordinal regression formulations. 3-D texture …
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Design and Integration of Machine Learning-Based Vision System for Automated Power Line Inspection Using a Mobile Damping Robot
… The first method focuses on a simpler binary classification, where image filtering techniques—such as Sobel, Scharr, and Gray-scale Variance Normalization—are compared to highlight defect patterns, followed by histogram-based feature extraction and classification into healthy or unhealthy …
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Ultrasonic acoustic health monitoring of ball bearings using neural network pattern classification of power spectral density
… acoustic emissions (UAE) to facilitate classification of bearing health via neural networks. This generic approach is applied to classifying the operating condition of conventional ball bearings. The acoustic emission signals used in this study are in the ultrasonic range (20-120 kHz), …
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